1 //
2 // Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "RefFullyConnectedWorkload.hpp"
7
8 #include "FullyConnected.hpp"
9 #include "RefWorkloadUtils.hpp"
10
11 #include "Profiling.hpp"
12
13 namespace armnn
14 {
15
GetNumActivations(const TensorInfo & inputInfo)16 unsigned int GetNumActivations(const TensorInfo& inputInfo)
17 {
18 unsigned int numActivations = 1; // Total number of activations in the input.
19 for (unsigned int i = 1; i < inputInfo.GetNumDimensions(); i++)
20 {
21 numActivations *= inputInfo.GetShape()[i];
22 }
23 return numActivations;
24 }
25
26
RefFullyConnectedWorkload(const FullyConnectedQueueDescriptor & descriptor,const WorkloadInfo & info)27 RefFullyConnectedWorkload::RefFullyConnectedWorkload(
28 const FullyConnectedQueueDescriptor& descriptor, const WorkloadInfo& info)
29 : RefBaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info)
30 , m_InputShape(info.m_InputTensorInfos[0].GetShape())
31 , m_WeightShape(info.m_InputTensorInfos[1].GetShape())
32 , m_OutputShape(info.m_OutputTensorInfos[0].GetShape())
33 , m_NumActivations(GetNumActivations(info.m_InputTensorInfos[0]))
34 {
35 }
36
Execute() const37 void RefFullyConnectedWorkload::Execute() const
38 {
39 Execute(m_Data.m_Inputs, m_Data.m_Outputs);
40 }
41
ExecuteAsync(ExecutionData & executionData)42 void RefFullyConnectedWorkload::ExecuteAsync(ExecutionData& executionData)
43 {
44 WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data);
45 Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs);
46 }
47
Execute(std::vector<ITensorHandle * > inputs,std::vector<ITensorHandle * > outputs) const48 void RefFullyConnectedWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const
49 {
50 ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefFullyConnectedWorkload_Execute");
51
52 std::unique_ptr<Decoder<float>> inputDecoder = MakeDecoder<float>(GetTensorInfo(inputs[0]), inputs[0]->Map());
53 std::unique_ptr<Encoder<float>> OutputEncoder = MakeEncoder<float>(GetTensorInfo(outputs[0]), outputs[0]->Map());
54
55 std::unique_ptr<Decoder<float>> weightsDecoder = MakeDecoder<float>(GetTensorInfo(inputs[1]), inputs[1]->Map());
56 std::unique_ptr<Decoder<float>> biasDecoder;
57
58 if (m_Data.m_Parameters.m_BiasEnabled)
59 {
60 biasDecoder = MakeDecoder<float>(GetTensorInfo(inputs[2]), inputs[2]->Map());
61 }
62
63 FullyConnected(m_InputShape,
64 *inputDecoder,
65 m_OutputShape,
66 *OutputEncoder,
67 m_WeightShape,
68 *weightsDecoder,
69 biasDecoder.get(),
70 m_Data.m_Parameters.m_BiasEnabled,
71 m_NumActivations,
72 m_Data.m_Parameters.m_TransposeWeightMatrix);
73 }
74
75 } //namespace armnn
76